## BALANCE BALANCE_FREQUENCY
## 1.024948e+03 9.694885e-01
## PURCHASES ONEOFF_PURCHASES
## 2.615234e+02 1.175435e+02
## INSTALLMENTS_PURCHASES CASH_ADVANCE
## 1.441638e+02 3.343629e+02
## PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY
## 3.444717e-01 7.219488e-02
## PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY
## 2.779689e-01 9.103191e-02
## CASH_ADVANCE_TRX PURCHASES_TRX
## 1.491892e+00 5.810811e+00
## CREDIT_LIMIT PAYMENTS
## 2.579091e+03 5.838450e+02
## MINIMUM_PAYMENTS PRC_FULL_PAYMENT
## 3.936023e+02 1.985161e-02
## TENURE
## 1.200000e+01## BALANCE BALANCE_FREQUENCY
## 955.184754 1.000000
## PURCHASES ONEOFF_PURCHASES
## 149.710000 0.000000
## INSTALLMENTS_PURCHASES CASH_ADVANCE
## 0.000000 37.483718
## PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY
## 0.250000 0.000000
## PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY
## 0.000000 0.083333
## CASH_ADVANCE_TRX PURCHASES_TRX
## 1.000000 3.000000
## CREDIT_LIMIT PAYMENTS
## 2000.000000 504.561160
## MINIMUM_PAYMENTS PRC_FULL_PAYMENT
## 311.963409 0.000000
## TENURE
## 12.000000# Mode (fonction personnalisée, car R n'a pas de mode intégré)
get_mode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
sapply(data[, num_cols], get_mode)## BALANCE BALANCE_FREQUENCY
## 40.90075 1.00000
## PURCHASES ONEOFF_PURCHASES
## 0.00000 0.00000
## INSTALLMENTS_PURCHASES CASH_ADVANCE
## 0.00000 0.00000
## PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY
## 0.00000 0.00000
## PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY
## 0.00000 0.00000
## CASH_ADVANCE_TRX PURCHASES_TRX
## 0.00000 0.00000
## CREDIT_LIMIT PAYMENTS
## 1200.00000 201.80208
## MINIMUM_PAYMENTS PRC_FULL_PAYMENT
## 139.50979 0.00000
## TENURE
## 12.00000## BALANCE BALANCE_FREQUENCY
## 8.447913e+02 8.522890e-02
## PURCHASES ONEOFF_PURCHASES
## 3.143626e+02 2.204483e+02
## INSTALLMENTS_PURCHASES CASH_ADVANCE
## 2.162904e+02 5.500022e+02
## PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY
## 3.619499e-01 1.111671e-01
## PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY
## 3.655734e-01 1.127375e-01
## CASH_ADVANCE_TRX PURCHASES_TRX
## 1.924920e+00 6.830659e+00
## CREDIT_LIMIT PAYMENTS
## 1.707943e+03 3.766651e+02
## MINIMUM_PAYMENTS PRC_FULL_PAYMENT
## 2.615090e+02 5.107206e-02
## TENURE
## 0.000000e+00## BALANCE BALANCE_FREQUENCY
## 7.136723e+05 7.263965e-03
## PURCHASES ONEOFF_PURCHASES
## 9.882387e+04 4.859745e+04
## INSTALLMENTS_PURCHASES CASH_ADVANCE
## 4.678154e+04 3.025024e+05
## PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY
## 1.310078e-01 1.235812e-02
## PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY
## 1.336439e-01 1.270974e-02
## CASH_ADVANCE_TRX PURCHASES_TRX
## 3.705318e+00 4.665790e+01
## CREDIT_LIMIT PAYMENTS
## 2.917069e+06 1.418766e+05
## MINIMUM_PAYMENTS PRC_FULL_PAYMENT
## 6.838695e+04 2.608356e-03
## TENURE
## 0.000000e+00## BALANCE BALANCE_FREQUENCY
## 1.591980 0.636364
## PURCHASES ONEOFF_PURCHASES
## 0.000000 0.000000
## INSTALLMENTS_PURCHASES CASH_ADVANCE
## 0.000000 0.000000
## PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY
## 0.000000 0.000000
## PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY
## 0.000000 0.000000
## CASH_ADVANCE_TRX PURCHASES_TRX
## 0.000000 0.000000
## CREDIT_LIMIT PAYMENTS
## 150.000000 4.841543
## MINIMUM_PAYMENTS PRC_FULL_PAYMENT
## 2.891346 0.000000
## TENURE
## 12.000000## BALANCE BALANCE_FREQUENCY
## 4393.939007 1.000000
## PURCHASES ONEOFF_PURCHASES
## 1957.300000 1085.630000
## INSTALLMENTS_PURCHASES CASH_ADVANCE
## 1043.180000 2780.106659
## PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY
## 1.000000 0.416667
## PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY
## 1.000000 0.416667
## CASH_ADVANCE_TRX PURCHASES_TRX
## 7.000000 30.000000
## CREDIT_LIMIT PAYMENTS
## 7600.000000 1741.996361
## MINIMUM_PAYMENTS PRC_FULL_PAYMENT
## 1167.316335 0.250000
## TENURE
## 12.000000# Quartiles (25%, 50%, 75%)
sapply(data[, num_cols], function(x) quantile(x, probs = c(0.25, 0.5, 0.75), na.rm = TRUE))## BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES
## 25% 274.9226 1 0.00 0.00
## 50% 955.1848 1 149.71 0.00
## 75% 1472.5915 1 420.96 139.85
## INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY
## 25% 0.000 0.00000 0.000
## 50% 0.000 37.48372 0.250
## 75% 238.085 436.31512 0.625
## ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY
## 25% 0.000000 0.0
## 50% 0.000000 0.0
## 75% 0.083333 0.5
## CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS
## 25% 0.000000 0 0 1200 302.1927
## 50% 0.083333 1 3 2000 504.5612
## 75% 0.166667 3 10 3000 762.9291
## MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
## 25% 187.6265 0 12
## 50% 311.9634 0 12
## 75% 542.5740 0 12# Percentiles (exemple : 10%, 90%)
sapply(data[, num_cols], function(x) quantile(x, probs = c(0.1, 0.9), na.rm = TRUE))## BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES
## 10% 58.89497 0.818182 0.000 0.000
## 90% 2218.14108 1.000000 702.028 415.236
## INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY
## 10% 0.00 0.000 0
## 90% 472.84 1153.266 1
## ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY
## 10% 0.00 0
## 90% 0.25 1
## CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT
## 10% 0.00 0 0 1000
## 90% 0.25 5 15 5000
## PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
## 10% 187.2024 139.8612 0.000000 12
## 90% 1143.3087 800.7793 0.090909 12# Vérifier que num_cols n'est pas vide
if (length(num_cols) > 0) {
# Calculer moyenne et écart-type
stats <- data.frame(
variable = num_cols,
mean = sapply(data[, num_cols], mean, na.rm = TRUE),
sd = sapply(data[, num_cols], sd, na.rm = TRUE),
stringsAsFactors = FALSE
)
# Barplot avec erreur ± écart-type
print(ggplot(stats, aes(x = reorder(variable, mean), y = mean)) +
geom_bar(stat="identity", fill="skyblue") +
geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd), width = 0.4) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title="Moyenne ± Écart-type des variables", y="Valeur", x="Variable"))
} else {
print("Erreur: Aucune colonne trouvée dans les données")
}
for (col in num_cols) {
p <- ggplot(data, aes(x = .data[[col]])) +
geom_histogram(fill="lightgreen", color="black", bins=30) +
labs(title=paste("Distribution de", col), x=col, y="Fréquence")
print(p)
}
# Boxplot pour chaque variable
for (col in num_cols) {
p <- ggplot(data, aes(y = .data[[col]])) +
geom_boxplot(fill="pink", color="black") +
labs(title=paste("Boxplot de", col), y=col) +
theme_minimal() +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())
print(p)
}
# Détection des outliers via IQR pour BALANCE
Q1 <- quantile(data$BALANCE, 0.25, na.rm = TRUE)
Q3 <- quantile(data$BALANCE, 0.75, na.rm = TRUE)
IQR <- Q3 - Q1
lower <- Q1 - 1.5 * IQR
upper <- Q3 + 1.5 * IQR
outliers_balance <- subset(data, BALANCE < lower | BALANCE > upper)
# Récapitulatif clair (seuils et compte)
summary_outliers <- data.frame(
variable = "BALANCE",
lower = lower,
upper = upper,
n_outliers = nrow(outliers_balance)
)
print(summary_outliers)## variable lower upper n_outliers
## 25% BALANCE -1521.581 3269.095 32# Top 10 outliers (colonnes clés uniquement)
if (nrow(outliers_balance) > 0) {
key_cols <- intersect(c("BALANCE","CREDIT_LIMIT","PAYMENTS","PURCHASES","CASH_ADVANCE","PRC_FULL_PAYMENT"), names(outliers_balance))
outliers_view <- outliers_balance[order(outliers_balance$BALANCE, decreasing = TRUE), key_cols, drop = FALSE]
print(utils::head(outliers_view, 10))
} else {
print("Aucun outlier détecté pour BALANCE selon la règle IQR.")
}## BALANCE CREDIT_LIMIT PAYMENTS PURCHASES CASH_ADVANCE PRC_FULL_PAYMENT
## 618 4393.939 5300 1026.406 417.08 2126.9616 0
## 1302 4383.572 5000 1161.701 0.00 1805.8535 0
## 1049 4355.982 7500 1188.603 0.00 305.9856 0
## 25 4245.855 5000 1004.355 1957.30 0.0000 0
## 933 4187.551 4500 1216.465 0.00 433.7192 0
## 1070 4128.312 5000 1082.274 0.00 1674.4016 0
## 1665 4071.994 5000 1528.928 0.00 1541.3938 0
## 19 4037.306 4500 1073.845 45.00 104.2381 0
## 972 4035.138 7000 1474.388 0.00 2399.4370 0
## 767 3985.638 6000 1203.181 20.00 1306.1840 0# 1. Boxplot avec identification des outliers pour BALANCE
ggplot(data, aes(y = BALANCE)) +
geom_boxplot(fill = "lightblue", outlier.colour = "red", outlier.size = 2) +
labs(title = "Outliers de BALANCE (en rouge)",
y = "Balance") +
theme_minimal() +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())
# 2. Boxplots multiples pour toutes les variables numériques
if (length(num_cols) > 0) {
# Normaliser les données pour comparaison
data_norm <- as.data.frame(scale(data[, num_cols]))
data_norm$ID <- 1:nrow(data_norm)
# Transformer en format long pour ggplot
data_long <- melt(data_norm, id.vars = "ID", variable.name = "Variable", value.name = "Value")
ggplot(data_long, aes(x = Variable, y = Value)) +
geom_boxplot(fill = "lightgreen", outlier.colour = "red", outlier.size = 1.5) +
labs(title = "Outliers de toutes les variables (données normalisées)",
x = "Variables",
y = "Valeur normalisée") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
}## Warning: Removed 2035 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
# 3. Scatter plot avec identification des outliers pour BALANCE vs CREDIT_LIMIT
if ("CREDIT_LIMIT" %in% names(data)) {
data$is_outlier_balance <- ifelse(data$BALANCE < lower | data$BALANCE > upper, "Outlier", "Normal")
ggplot(data, aes(x = CREDIT_LIMIT, y = BALANCE, color = is_outlier_balance)) +
geom_point(alpha = 0.5, size = 2) +
scale_color_manual(values = c("Normal" = "blue", "Outlier" = "red")) +
labs(title = "Outliers de BALANCE en fonction de CREDIT_LIMIT",
x = "Credit Limit",
y = "Balance",
color = "Type") +
theme_minimal()
}
# 4. Density plot avec zones d'outliers
ggplot(data, aes(x = BALANCE)) +
geom_density(fill = "lightblue", alpha = 0.5) +
geom_vline(xintercept = lower, color = "red", linetype = "dashed", linewidth = 1) +
geom_vline(xintercept = upper, color = "red", linetype = "dashed", linewidth = 1) +
annotate("text", x = lower, y = 0, label = paste("Q1-1.5*IQR:", round(lower, 2)),
hjust = 1.1, color = "red") +
annotate("text", x = upper, y = 0, label = paste("Q3+1.5*IQR:", round(upper, 2)),
hjust = -0.1, color = "red") +
labs(title = "Distribution de BALANCE avec limites d'outliers",
x = "Balance",
y = "Densité") +
theme_minimal()
# 5. Détection et visualisation des outliers pour toutes les variables
outliers_summary <- data.frame()
for (col in num_cols) {
Q1 <- quantile(data[[col]], 0.25, na.rm = TRUE)
Q3 <- quantile(data[[col]], 0.75, na.rm = TRUE)
IQR_val <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_val
upper_bound <- Q3 + 1.5 * IQR_val
n_outliers <- sum(data[[col]] < lower_bound | data[[col]] > upper_bound, na.rm = TRUE)
pct_outliers <- round(100 * n_outliers / nrow(data), 2)
outliers_summary <- rbind(outliers_summary, data.frame(
Variable = col,
Q1 = Q1,
Q3 = Q3,
IQR = IQR_val,
Lower = lower_bound,
Upper = upper_bound,
N_Outliers = n_outliers,
Pct_Outliers = pct_outliers
))
}
# Afficher le tableau récapitulatif
print(outliers_summary)## Variable Q1 Q3 IQR
## 25% BALANCE 274.9226 1472.591481 1197.668854
## 25%1 BALANCE_FREQUENCY 1.0000 1.000000 0.000000
## 25%2 PURCHASES 0.0000 420.960000 420.960000
## 25%3 ONEOFF_PURCHASES 0.0000 139.850000 139.850000
## 25%4 INSTALLMENTS_PURCHASES 0.0000 238.085000 238.085000
## 25%5 CASH_ADVANCE 0.0000 436.315122 436.315122
## 25%6 PURCHASES_FREQUENCY 0.0000 0.625000 0.625000
## 25%7 ONEOFF_PURCHASES_FREQUENCY 0.0000 0.083333 0.083333
## 25%8 PURCHASES_INSTALLMENTS_FREQUENCY 0.0000 0.500000 0.500000
## 25%9 CASH_ADVANCE_FREQUENCY 0.0000 0.166667 0.166667
## 25%10 CASH_ADVANCE_TRX 0.0000 3.000000 3.000000
## 25%11 PURCHASES_TRX 0.0000 10.000000 10.000000
## 25%12 CREDIT_LIMIT 1200.0000 3000.000000 1800.000000
## 25%13 PAYMENTS 302.1927 762.929061 460.736406
## 25%14 MINIMUM_PAYMENTS 187.6265 542.573998 354.947543
## 25%15 PRC_FULL_PAYMENT 0.0000 0.000000 0.000000
## 25%16 TENURE 12.0000 12.000000 0.000000
## Lower Upper N_Outliers Pct_Outliers
## 25% -1521.5806553 3269.0947627 32 1.57
## 25%1 1.0000000 1.0000000 278 13.66
## 25%2 -631.4400000 1052.4000000 44 2.16
## 25%3 -209.7750000 349.6250000 245 12.04
## 25%4 -357.1275000 595.2125000 112 5.50
## 25%5 -654.4726837 1090.7878062 233 11.45
## 25%6 -0.9375000 1.5625000 0 0.00
## 25%7 -0.1249995 0.2083325 277 13.61
## 25%8 -0.7500000 1.2500000 0 0.00
## 25%9 -0.2500005 0.4166675 0 0.00
## 25%10 -4.5000000 7.5000000 0 0.00
## 25%11 -15.0000000 25.0000000 37 1.82
## 25%12 -1500.0000000 5700.0000000 175 8.60
## 25%13 -388.9119540 1454.0336700 85 4.18
## 25%14 -344.7948600 1074.9953120 36 1.77
## 25%15 0.0000000 0.0000000 313 15.38
## 25%16 12.0000000 12.0000000 0 0.00# Graphique à barres du pourcentage d'outliers par variable
ggplot(outliers_summary, aes(x = reorder(Variable, -Pct_Outliers), y = Pct_Outliers)) +
geom_bar(stat = "identity", fill = "coral") +
geom_text(aes(label = paste0(Pct_Outliers, "%")), vjust = -0.5, size = 3) +
labs(title = "Pourcentage d'outliers par variable",
x = "Variable",
y = "% d'outliers") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
## Détection détaillée des outliers pour chaque variable
# Pour chaque variable numérique, créer des visualisations détaillées
for (col in num_cols) {
cat("\n### Variable:", col, "\n\n")
# Calcul des seuils IQR
Q1 <- quantile(data[[col]], 0.25, na.rm = TRUE)
Q3 <- quantile(data[[col]], 0.75, na.rm = TRUE)
IQR_val <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_val
upper_bound <- Q3 + 1.5 * IQR_val
# Identification des outliers
outliers_mask <- data[[col]] < lower_bound | data[[col]] > upper_bound
n_outliers <- sum(outliers_mask, na.rm = TRUE)
pct_outliers <- round(100 * n_outliers / nrow(data), 2)
# Afficher les statistiques
cat("- Nombre d'outliers:", n_outliers, "/", nrow(data), "(", pct_outliers, "%)\n")
cat("- Limites: [", round(lower_bound, 2), ",", round(upper_bound, 2), "]\n")
cat("- Q1 =", round(Q1, 2), ", Q3 =", round(Q3, 2), ", IQR =", round(IQR_val, 2), "\n\n")
# Créer une colonne pour identifier les outliers
data$outlier_status <- ifelse(outliers_mask, "Outlier", "Normal")
# 1. Boxplot individuel avec outliers en rouge
p1 <- ggplot(data, aes(y = .data[[col]])) +
geom_boxplot(fill = "lightblue", outlier.colour = "red", outlier.size = 2) +
labs(title = paste("Boxplot de", col),
y = col) +
theme_minimal() +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())
print(p1)
# 2. Density plot avec zones d'outliers
p2 <- ggplot(data, aes(x = .data[[col]])) +
geom_density(fill = "lightgreen", alpha = 0.5) +
geom_vline(xintercept = lower_bound, color = "red", linetype = "dashed", linewidth = 1) +
geom_vline(xintercept = upper_bound, color = "red", linetype = "dashed", linewidth = 1) +
geom_vline(xintercept = Q1, color = "blue", linetype = "dotted", linewidth = 0.8) +
geom_vline(xintercept = Q3, color = "blue", linetype = "dotted", linewidth = 0.8) +
labs(title = paste("Distribution de", col, "avec limites d'outliers"),
x = col,
y = "Densité") +
theme_minimal()
print(p2)
# 3. Scatter plot avec index pour voir la position des outliers
p3 <- ggplot(data, aes(x = 1:nrow(data), y = .data[[col]], color = outlier_status)) +
geom_point(alpha = 0.6, size = 1.5) +
scale_color_manual(values = c("Normal" = "blue", "Outlier" = "red")) +
labs(title = paste("Outliers de", col, "par observation"),
x = "Index",
y = col,
color = "Type") +
theme_minimal()
print(p3)
# Afficher quelques exemples d'outliers si présents
if (n_outliers > 0 & n_outliers <= 20) {
cat("\nTous les outliers détectés:\n")
outliers_data <- data[outliers_mask, c(col), drop = FALSE]
print(outliers_data[order(outliers_data[[col]], decreasing = TRUE), , drop = FALSE])
} else if (n_outliers > 20) {
cat("\nTop 10 outliers (valeurs les plus extrêmes):\n")
outliers_data <- data[outliers_mask, c(col), drop = FALSE]
print(utils::head(outliers_data[order(abs(outliers_data[[col]] - median(data[[col]], na.rm = TRUE)), decreasing = TRUE), , drop = FALSE], 10))
}
cat("\n---\n")
}##
## ### Variable: BALANCE
##
## - Nombre d'outliers: 32 / 2035 ( 1.57 %)
## - Limites: [ -1521.58 , 3269.09 ]
## - Q1 = 274.92 , Q3 = 1472.59 , IQR = 1197.67
##
## Top 10 outliers (valeurs les plus extrêmes):
## BALANCE
## 618 4393.939
## 1302 4383.572
## 1049 4355.982
## 25 4245.855
## 933 4187.551
## 1070 4128.312
## 1665 4071.994
## 19 4037.306
## 972 4035.138
## 767 3985.638
##
## ---
##
## ### Variable: BALANCE_FREQUENCY
##
## - Nombre d'outliers: 278 / 2035 ( 13.66 %)
## - Limites: [ 1 , 1 ]
## - Q1 = 1 , Q3 = 1 , IQR = 0
##
## Top 10 outliers (valeurs les plus extrêmes):
## BALANCE_FREQUENCY
## 26 0.636364
## 94 0.636364
## 186 0.636364
## 221 0.636364
## 238 0.636364
## 311 0.636364
## 340 0.636364
## 376 0.636364
## 397 0.636364
## 398 0.636364
##
## ---
##
## ### Variable: PURCHASES
##
## - Nombre d'outliers: 44 / 2035 ( 2.16 %)
## - Limites: [ -631.44 , 1052.4 ]
## - Q1 = 0 , Q3 = 420.96 , IQR = 420.96
##
## Top 10 outliers (valeurs les plus extrêmes):
## PURCHASES
## 25 1957.30
## 1383 1853.70
## 1636 1814.65
## 288 1788.10
## 595 1725.05
## 444 1672.99
## 170 1594.00
## 160 1555.66
## 540 1518.26
## 124 1488.80
##
## ---
##
## ### Variable: ONEOFF_PURCHASES
##
## - Nombre d'outliers: 245 / 2035 ( 12.04 %)
## - Limites: [ -209.77 , 349.62 ]
## - Q1 = 0 , Q3 = 139.85 , IQR = 139.85
##
## Top 10 outliers (valeurs les plus extrêmes):
## ONEOFF_PURCHASES
## 239 1085.63
## 170 1084.00
## 1094 1081.00
## 25 1077.30
## 640 1050.76
## 906 1045.84
## 365 1040.99
## 1046 1029.29
## 31 1025.00
## 540 1024.83
##
## ---
##
## ### Variable: INSTALLMENTS_PURCHASES
##
## - Nombre d'outliers: 112 / 2035 ( 5.5 %)
## - Limites: [ -357.13 , 595.21 ]
## - Q1 = 0 , Q3 = 238.08 , IQR = 238.08
##
## Top 10 outliers (valeurs les plus extrêmes):
## INSTALLMENTS_PURCHASES
## 537 1043.18
## 199 1043.00
## 1509 1039.92
## 595 1025.00
## 543 1020.76
## 181 1016.00
## 673 1013.00
## 641 996.66
## 235 972.38
## 1283 968.64
##
## ---
##
## ### Variable: CASH_ADVANCE
##
## - Nombre d'outliers: 233 / 2035 ( 11.45 %)
## - Limites: [ -654.47 , 1090.79 ]
## - Q1 = 0 , Q3 = 436.32 , IQR = 436.32
##
## Top 10 outliers (valeurs les plus extrêmes):
## CASH_ADVANCE
## 814 2780.107
## 1042 2774.386
## 1423 2721.304
## 1085 2706.494
## 1598 2704.303
## 2031 2625.645
## 870 2617.931
## 1105 2601.135
## 609 2577.953
## 1080 2548.837
##
## ---
##
## ### Variable: PURCHASES_FREQUENCY
##
## - Nombre d'outliers: 0 / 2035 ( 0 %)
## - Limites: [ -0.94 , 1.56 ]
## - Q1 = 0 , Q3 = 0.62 , IQR = 0.62
##
## ---
##
## ### Variable: ONEOFF_PURCHASES_FREQUENCY
##
## - Nombre d'outliers: 277 / 2035 ( 13.61 %)
## - Limites: [ -0.12 , 0.21 ]
## - Q1 = 0 , Q3 = 0.08 , IQR = 0.08
##
## Top 10 outliers (valeurs les plus extrêmes):
## ONEOFF_PURCHASES_FREQUENCY
## 59 0.416667
## 73 0.416667
## 93 0.416667
## 102 0.416667
## 237 0.416667
## 245 0.416667
## 255 0.416667
## 261 0.416667
## 340 0.416667
## 357 0.416667
##
## ---
##
## ### Variable: PURCHASES_INSTALLMENTS_FREQUENCY
##
## - Nombre d'outliers: 0 / 2035 ( 0 %)
## - Limites: [ -0.75 , 1.25 ]
## - Q1 = 0 , Q3 = 0.5 , IQR = 0.5
##
## ---
##
## ### Variable: CASH_ADVANCE_FREQUENCY
##
## - Nombre d'outliers: 0 / 2035 ( 0 %)
## - Limites: [ -0.25 , 0.42 ]
## - Q1 = 0 , Q3 = 0.17 , IQR = 0.17
##
## ---
##
## ### Variable: CASH_ADVANCE_TRX
##
## - Nombre d'outliers: 0 / 2035 ( 0 %)
## - Limites: [ -4.5 , 7.5 ]
## - Q1 = 0 , Q3 = 3 , IQR = 3
##
## ---
##
## ### Variable: PURCHASES_TRX
##
## - Nombre d'outliers: 37 / 2035 ( 1.82 %)
## - Limites: [ -15 , 25 ]
## - Q1 = 0 , Q3 = 10 , IQR = 10
##
## Top 10 outliers (valeurs les plus extrêmes):
## PURCHASES_TRX
## 38 30
## 299 30
## 1004 30
## 1391 30
## 1729 30
## 594 29
## 910 29
## 1710 29
## 1816 29
## 322 28
##
## ---
##
## ### Variable: CREDIT_LIMIT
##
## - Nombre d'outliers: 175 / 2035 ( 8.6 %)
## - Limites: [ -1500 , 5700 ]
## - Q1 = 1200 , Q3 = 3000 , IQR = 1800
##
## Top 10 outliers (valeurs les plus extrêmes):
## CREDIT_LIMIT
## 693 7600
## 30 7500
## 31 7500
## 49 7500
## 90 7500
## 133 7500
## 178 7500
## 181 7500
## 322 7500
## 324 7500
##
## ---
##
## ### Variable: PAYMENTS
##
## - Nombre d'outliers: 85 / 2035 ( 4.18 %)
## - Limites: [ -388.91 , 1454.03 ]
## - Q1 = 302.19 , Q3 = 762.93 , IQR = 460.74
##
## Top 10 outliers (valeurs les plus extrêmes):
## PAYMENTS
## 1377 1741.996
## 259 1738.450
## 176 1736.121
## 768 1734.837
## 1080 1732.275
## 1695 1723.351
## 260 1721.926
## 1415 1713.312
## 1127 1713.300
## 1445 1711.865
##
## ---
##
## ### Variable: MINIMUM_PAYMENTS
##
## - Nombre d'outliers: 36 / 2035 ( 1.77 %)
## - Limites: [ -344.79 , 1075 ]
## - Q1 = 187.63 , Q3 = 542.57 , IQR = 354.95
##
## Top 10 outliers (valeurs les plus extrêmes):
## MINIMUM_PAYMENTS
## 735 1167.316
## 1741 1164.898
## 1940 1159.793
## 347 1159.311
## 1665 1157.359
## 683 1155.325
## 618 1152.616
## 767 1148.849
## 936 1147.377
## 605 1145.811
##
## ---
##
## ### Variable: PRC_FULL_PAYMENT
##
## - Nombre d'outliers: 313 / 2035 ( 15.38 %)
## - Limites: [ 0 , 0 ]
## - Q1 = 0 , Q3 = 0 , IQR = 0
##
## Top 10 outliers (valeurs les plus extrêmes):
## PRC_FULL_PAYMENT
## 48 0.25
## 94 0.25
## 150 0.25
## 188 0.25
## 221 0.25
## 251 0.25
## 303 0.25
## 427 0.25
## 444 0.25
## 459 0.25
##
## ---
##
## ### Variable: TENURE
##
## - Nombre d'outliers: 0 / 2035 ( 0 %)
## - Limites: [ 12 , 12 ]
## - Q1 = 12 , Q3 = 12 , IQR = 0
##
## ---# Créer une matrice binaire indiquant si chaque observation est un outlier pour chaque variable
outliers_matrix <- data.frame(matrix(0, nrow = nrow(data), ncol = length(num_cols)))
colnames(outliers_matrix) <- num_cols
for (i in seq_along(num_cols)) {
col <- num_cols[i]
Q1 <- quantile(data[[col]], 0.25, na.rm = TRUE)
Q3 <- quantile(data[[col]], 0.75, na.rm = TRUE)
IQR_val <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_val
upper_bound <- Q3 + 1.5 * IQR_val
outliers_matrix[, i] <- ifelse(data[[col]] < lower_bound | data[[col]] > upper_bound, 1, 0)
}
# Vérifier quelles colonnes ont une variance non nulle (au moins un outlier et au moins une valeur normale)
valid_cols <- sapply(outliers_matrix, function(x) {
var_x <- var(x, na.rm = TRUE)
!is.na(var_x) && var_x > 0
})
cat("Variables avec outliers détectés:\n")## Variables avec outliers détectés:## BALANCE, BALANCE_FREQUENCY, PURCHASES, ONEOFF_PURCHASES, INSTALLMENTS_PURCHASES, CASH_ADVANCE, ONEOFF_PURCHASES_FREQUENCY, PURCHASES_TRX, CREDIT_LIMIT, PAYMENTS, MINIMUM_PAYMENTS, PRC_FULL_PAYMENTif (sum(valid_cols) > 1) {
# Calculer la corrélation uniquement pour les colonnes avec variance non nulle
cor_outliers <- cor(outliers_matrix[, valid_cols], use = "complete.obs")
# Heatmap de corrélation
cor_melted <- melt(cor_outliers)
print(ggplot(cor_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
geom_text(aes(label = round(value, 2)), size = 3) +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1)) +
labs(title = "Corrélation entre les outliers des variables",
x = "", y = "", fill = "Corrélation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)))
# Afficher les variables qui ont tendance à avoir des outliers ensemble
cat("\nVariables avec forte corrélation d'outliers (> 0.5):\n")
high_cor <- which(cor_outliers > 0.5 & cor_outliers < 1, arr.ind = TRUE)
if (nrow(high_cor) > 0) {
for (i in 1:nrow(high_cor)) {
var1 <- rownames(cor_outliers)[high_cor[i, 1]]
var2 <- colnames(cor_outliers)[high_cor[i, 2]]
cor_val <- cor_outliers[high_cor[i, 1], high_cor[i, 2]]
cat(var1, "<->", var2, ":", round(cor_val, 3), "\n")
}
} else {
cat("Aucune forte corrélation détectée.\n")
}
} else {
cat("Impossible de calculer la corrélation : pas assez de variables avec des outliers.\n")
if (sum(valid_cols) == 0) {
cat("Aucune variable n'a d'outliers détectés.\n")
} else {
cat("Une seule variable a des outliers :", num_cols[valid_cols], "\n")
}
}
##
## Variables avec forte corrélation d'outliers (> 0.5):
## ONEOFF_PURCHASES_FREQUENCY <-> ONEOFF_PURCHASES : 0.527
## ONEOFF_PURCHASES <-> ONEOFF_PURCHASES_FREQUENCY : 0.527